Size and shape analysis of error-prone shape data

Du, J.; Dryden, Ian L.; Huang, X.

Abstract

We consider the problem of comparing sizes and shapes of objects when landmark data are prone to measurement error. We show that naive implementation of ordinary Procrustes analysis that ignores measurement error can compromise inference. To account for measurement error, we propose the conditional score method for matching configurations, which guarantees consistent inference under mild model assumptions. The effects of measurement error on inference from naive Procrustes analysis and the performance of the proposed method are illustrated via simulation and application in three real data examples. Supplementary materials for this article are available online.

Citation

Du, J., Dryden, I. L., & Huang, X. (2015). Size and shape analysis of error-prone shape data. Journal of the American Statistical Association, 110(509), https://doi.org/10.1080/01621459.2014.908779

• Journal Article Type: Article
• Acceptance Date: Mar 17, 2014
• Online Publication Date: Apr 18, 2014
• Publication Date: Mar 1, 2015
• Deposit Date: Mar 7, 2017
• Publicly Available Date: Mar 7, 2017
• Journal: Journal of the American Statistical Association
• Print ISSN: 0162-1459
• Electronic ISSN: 1537-274X
• Publisher: Taylor and Francis
• Peer Reviewed: Peer Reviewed
• Volume: 110
• Issue: 509
• DOI: https://doi.org/10.1080/01621459.2014.908779
• Keywords: Complex normal; Configuration; Landmark; Ordinary Procrustes analysis; Quaternion
• Public URL: https://nottingham-repository.worktribe.com/output/984526
• Publisher URL: http://www.tandfonline.com/doi/full/10.1080/01621459.2014.908779
• Contract Date: Mar 7, 2017